Mastering Word2Vec: An In-Depth Walkthrough for Beginners

In today’s AI landscape, people often use large language models (LLMs) to solve many problems. But not every task needs such big and powerful models. Sometimes, smaller and faster methods work better, they are easier to use, take less memory, and are still incredibly useful. One of those classic methods is Word2Vec. Even though it is an old idea, it still helps a lot in many real world projects. So today, I’ll talk about Word2Vec, what it is, how it works, and why it still matters.

Word2Vec

But before that, let’s take a look at the word embeddings. Before diving into Word2Vec itself, let’s take a step back and understand a key concept it’s built on: word embeddings. Let me ask you a simple question: How can we make machines understand the meaning of our words? Machines don’t understand language the way humans do, they work with numbers. That’s where word embeddings come in. The main idea behind word embeddings is to convert words into numbers (vectors), in a way that preserves their meaning and relationship with other words. These vectors capture both the semantic meaning (what a word means) and syntactic patterns (how it is used in a sentence).

Word Embedding techniques can be classified into two categories:

1. Count-based Methods: The underlying assumption is that words that co-occur frequently in the same context are likely to have similar meanings. Common examples include TFIDF, BOW, N-grams.
2. Predictive Methods: These include training a neural-network on a large corpus of text to predict the context in which each word appears. Examples include Word2Vec

Introduction to Word2Vec

Now that we understand what word embeddings are and why they matter, let’s look more closely at one of the most well-known embedding techniques: Word2Vec.

Word2Vec is a neural network model developed by Tomas Mikolov and his team at Google, used to represent words in a continuous vector space. Its main goal is to learn a vector (numerical representation) for each word in a large collection of text. These vectors capture the semantic meaning of words, meaning that similar words will end up having similar vector representations. Once trained, these word vectors can be used in many NLP tasks like sentimental analysis, text classification, machine translation etc.

There are two main architectures used in Word2Vec: Continuous Bag-of-Words (CBOW) and SkipGram. Both of these architectures are used to train a neural network to predict the context of a word.

What is Continuous Bag-of-Words (CBOW)?

CBOW is a neural network architecture used in Word2Vec that predicts a target word based on its context. The context of a word is defined as the words that occur before and after the target word. The input to the CBOW model is a set of context words, and the output is the target word. The goal of the CBOW model is to learn the probability distribution of the target word given its context.

Continuous Bag Of Words

How does CBOW work?

The CBOW model consists of three layers: an input layer, a hidden layer, and an output layer. The input layer consists of the context words, which are represented as one-hot vectors. The hidden layer is a projection layer that maps the one-hot vectors to a continuous vector space. The output layer is a softmax layer that predicts the probability distribution of the target word given its context.

During training, the CBOW model is fed with a set of context words and their corresponding target words. The model learns to adjust the weights of the hidden layer to maximize the probability of predicting the correct target word given its context.

Pros and Cons of CBOW

One of the main advantages of CBOW is that it is faster to train than SkipGram. CBOW also tends to perform better than SkipGram on smaller datasets. However, CBOW may not perform well on rare words that have limited context. For example, consider the word apple, which can refer to both a fruit and a company, but CBOW averages the two meanings and places it in a cluster for both fruits and companies.

What is Skip-Gram?

Skip-Gram is another neural network architecture used in Word2Vec that predicts the context of a sword given its target word. The input to the Skip-Gram model is a target word, and the output is a set of context words. The goal of the Skip-Gram model is to learn the probability distribution of the context words given the target word.

Skip-Gram

How does Skip-Gram work?

The Skip-Gram model consists of three layers: an input layer, a hidden layer, and an output layer. The input layer receives the target word, which is represented as a one-hot vector. The hidden layer serves as a projection layer that maps this one-hot vector into a continuous vector space. The output layer is a SoftMax layer that predicts the probability distribution of the context words based on the given target word. During training, the Skip-Gram model is provided with a target word and is trained to predict the surrounding context words. The model learns to adjust the weights in the hidden layer to maximize the probability of correctly predicting the context words from the given target word.

Pros and Cons of Skip-Gram

One of the main advantages of Skip-Gram is that it performs well on rare words that have limited context. It can capture two interpretations for a single word. In other words, there are two vector representations of the word Apple. One is for the business, while the other is for the fruit previously mis-interpreted by CBOW. Skip-Gram also tends to perform better than CBOW on larger datasets. However, Skip-Gram is slower to train than CBOW.

Comparison between CBOW and Skip-Gram

CBOW and Skip-Gram are two different architectures used in Word2Vec, and each has its own strengths and weaknesses. CBOW is faster to train and performs better on smaller datasets, but may not perform well on rare words. Skip-Gram is slower to train but performs better on larger datasets and rare words.

Skip-Gram vs CBOW

Why do skip-gram models outperform CBOW models for unusual words?

For rare and infrequent words, the skip-gram model generates better word embedding’s because it trains on the entire corpus to learn the surrounding context of each word. In contrast, the CBOW model uses the context words to predict the center word, which can be less effective for rare words because they may not appear often enough in the context to generate an accurate representation.

Final Thoughts

Word2Vec might not be the newest technique in natural language processing, but it remains a powerful and practical tool. It gives us a way to turn words into numbers that carry meaning, making it easier for machines to work with language. Whether you choose CBOW for speed or Skip-Gram for depth, both methods help build meaningful word embeddings. While large language models dominate the spotlight today, tools like Word2Vec remind us that sometimes, simplicity and efficiency are all you need. So, the next time you’re working on an NLP task, don’t forget this classic method, it still has a lot to offer.